// Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.

// Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"),
// to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
// and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

// The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
// WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
// COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

#include "RandomTensorLayer.h"

#include "impl/RandomTensorLayerCPU.h"

namespace raul
{

RandomTensorLayer::RandomTensorLayer(const Name& name, const RandomTensorLayerParams& params, NetworkParameters& networkParameters)
    : BasicLayer(name, "RandomTensor", params, networkParameters, { false, true })
    , mDepth(params.mDepth)
    , mHeight(params.mHeight)
    , mWidth(params.mWidth)
    , mMean(params.mMean)
    , mStdDev(params.mStdDev)
    , mGenerator(static_cast<unsigned>(params.mSeed))
{
    if (!mInputs.empty())
    {
        THROW("RandomTensorLayer", name, "no inputs expected");
    }

    if (mOutputs.size() != 1)
    {
        THROW("RandomLayer", name, "wrong number of output names");
    }

    if (mOutputs[0].empty())
    {
        THROW("RandomLayer", name, "empty output name");
    }

    DECLARE_IMPL(RandomTensorLayer, RandomTensorLayerCPU<MemoryManager>, RandomTensorLayerCPU<MemoryManagerFP16>)

    mNetworkParams.mWorkflow.tensorNeeded(name, mOutputs[0], raul::WShape{ BS(), mDepth, mHeight, mWidth }, DEC_FORW_WRIT);
}

} // namespace raul